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Nlogit 6 —羅吉特模式軟件包

軟件試用 獲取報價

軟件簡介

NLOGIT是LIMDEP的增強版，為多項式選擇數據的評估、模擬、分析提供程序，例如商標選擇、運輸模式和消費者在一系列競爭中選擇的所有形式的測量和市場數據。NLOGIT已經成為評估和模擬多項離散選擇模型的首選軟件包。
NLOGIT新功能包括：內置超過200個估計量，你可以用來分析
四個層次嵌套logit模型,混合logit隨機參數,潛在類別,多項普羅比模型,面板數據——MNL固定效果。

Nlogit 6 軟件功能

NLOGIT是建模的最佳選擇
NLOGIT是多項式離散選擇模型的評估與模擬方面的標準套裝軟件。對于其他建模軟件，NLOGIT有最大似然估計的全部信息，多達4個級別的嵌入式logit模型。許多其他的公式也包括在NLOGIT里面，如隨機參數（混合logit）模型，多項式概率，還有許多形式的嵌式logit模型和一些新的面板數據公式。NLOGIT是唯一一個離散選擇分析大型軟件包，包含了綜合計量經濟學軟件LIMDEP的全部功能。

數據分析
NLOGIT通常對消費者的個別、橫截面數據和多個方案的決策進行分析。但是，它同樣可以對市場份額、頻率數據、可選擇性事物的排名、估計值，以及反復觀察得到的面板數據進行分析。除了LIMDEP的分析程序外，NLOGIT還有其他一些處理數據的程序。

模型估計
比起其他任何軟件，NLOGIT支持更大范圍的離散選擇模型。包括基本的多項式logit模型、多達4個級別的嵌入式logit模型，多項式概率模型和最先進的混合logit模型(隨機參數)。在所有情況下，能提供多種形式的可用模型。

NLOGIT包含LIMDEP支持的所有離散選擇評估模型，并且還包含一些LIMDEP沒有的離散選擇模型，如：
● 多項式logit — 各種規格的
● MNL隨機效應
● 嵌入式logit
● 廣義的嵌入式logit
● 多項式概率
● 混合式(隨機參數)logit
● 內核logit
● 異方差的極值
● 協方差的異質性
● 潛在類別

模型規格
NLOGIT 估計程序作為LIMDEP模型命令被訪問。因為在LIMDEP中，離散選擇模型往往比其他單方程模式更加復雜，命令設置包含NLOGIT的許多具體規格。

假設檢驗的推理工具
NLOGIT可以訪問LIMDEP后評估和分析工具的所有功能，包括wald、比率和Lagrange乘數檢驗以及所有的矩陣代數和科學計算器工具。NLOGIT為離散選擇分析提供具體的工具，包括檢驗多項logit模型IIA假定的內置程序。

模擬
NLOGIT的任何模型估計都可以用模擬功能的“What if”來分析?；鶞誓Ｐ蜑闃颖緮祿a生了一組擬合概率，聚合選擇集中可選擇性的樣本股的預測。然后使用模擬器和評估數據集以及其他兼容數據集，來重新計算您指定情況下的股票，比如特別選擇價格變化或家庭收入的變化。

報告結果
該模擬器現在可以用來計算和報告彈性。（因為該程序已經設計成用來計算由于屬性的離散變化而引起的概率變化，弧彈性就是一個自然延伸的結果）

數據設置和類型
在NLOGIT中，設置多項式離散選擇模型的數據主要有3大改進。在NLOGIT模型中，主要數據常常被錯誤編碼或安排不當地分析。而現在您可以要求NLOGIT檢查和觀察數據，有20種不同的問題會阻礙估計。有些是自動的，其他只要您要求，系統會幫您進行。對于實驗性的工作和模型開發，當您提供實用程序時，NLOGIT模型將在類型1極值分布的基礎上模擬選擇性數據。最后，在某些情況下，調查顯示，個人在做出選擇時會忽略某些屬性。NLOGIT可以自動調節這些數據在模型中的評估。（簡單地設置屬性為零是不正確的方法 – 比如考慮價格歸零。這個模型本身就是可以被修改的。）

NLOGIT的新特征

NLOGIT除了包含LIMDEP 的所有功能外，我們還增加了2個新的模型，在面板數據下的一個嵌入式logit模型和一個等同的隨機效應模型。對于分析個別選擇來說，混合logit模型（隨機參數logit模型）是目前運用最普遍最靈活的模型。新增的模型如下：

廣義嵌入式logit模型
嵌入式logit模型是多項logit模型中最流行擴展模型的一種。模型的缺點之一就是嚴格要求樹形結構能準確地把每個選擇分配到樹結構中的分支中。而廣義嵌入式logit模型允許一次在幾個分支中出現多個選擇和概率。

誤差分量Logit 模型
多項式logit模型作為離散選擇模型的基本平臺已經幾十年了。由于不可預測的因素，無法捕捉單個選擇的具體變化。誤差分量Logit模型彌補了這一缺陷。在重復選擇（面板數據）環境下，這一模型將在隨機效益模型中的起重要作用。
模型擴展
異質性差異
可以說實用功能的異質性差異跟異質性水平一樣重要。我們在容納異方差到混合logit模型、協方差異質性（嵌入式）模型以及異質性極值模型中增加了具體的規格。

多項式logit模型（GME）
廣義上講最大熵估計提供了一種校準參數的方法，也就是在很多例子中，比起最大似然法，跟數據模型的“fit”更緊密。我們在多項式logit模型和條件logit模型中增加了一個GME評估器，也就是基礎MNL模型的所有形式。

混合Logit 模型
如前所述，混合logit模型代表著多項選擇模型的前沿。我們增加了許多新的功能以保證這種模型的NLOGIT的實施。其中包括：
增加了許多規則來建立實際的、合理的參數分布。比如，Weibull和三角分布為標記制約系數提供了可行的替代方式。我們還在隨機參數的定義中建立了可選的規格，允許出現在不同分布的標準偏差和均值的特征變化。
提供隨機系數的異質性的差異（異方差）
誤差分量logit 模型可能在混合logit模型的頂部進行分層。
除了對個別具體期望隨機參數估計之外，您現在還可以計算支付意愿的具體措施，而這些措施是作為比例系數來計算的。
現在混合logit模型可能適合排列數據。

軟件簡介（英文）

NLOGIT: Superior Statistical Analysis Software
Complete Statistical Analysis Tools
NLOGIT includes all the features and capabilities of LIMDEP 11 plus NLOGIT’s estimation and analysis tools for multinomial choice modeling.
NLOGIT software is the only large package for choice modeling that contains the full set of features of an integrated statistics program.

The Power of NLOGIT
NLOGIT provides programs for estimation, simulation and analysis of multinomial choice data, such as brand choice, transportation mode, and all manner of survey and market data in which consumers choose among a set of competing alternatives. Since its introduction nearly 20 years ago, NLOGIT has become the premier statistical package for estimation and simulation of multinomial logit models including willingness to pay and best/worst modeling. NLOGIT is the only program available that supports mixing stated and revealed choice data sets.



Superior Analysis Tools for Multinomial Choice Modeling
Our NLOGIT statistical software provides the widest and deepest array of tools available anywhere for analysis of multinomial logit models, including nested logit, generalized mixed multinomial logit, heteroscedastic extreme value, multinomial probit, mixed logit and more. A unique simulation package that allows you to analyze alternative scenarios in the context of any estimated discrete choice model with any data set, whether used in estimation or as hold out data for examining model cross validity.

Function list

Data Analysis
NLOGIT will typically be used to analyze individual, cross section data on consumer choices and decisions from multiple alternatives. But, the program is equally equipped for market shares or frequency data, data on rankings of alternatives, and, for several of the estimators, panel data from repeated observation of choice situations. There are several data handling procedures for NLOGIT in addition to all those available in LIMDEP.

Model Estimation
NLOGIT supports a greater range of models for discrete choice than any other package. These include the basic multinomial logit model, nested logit models up to four levels, the multinomial probit model and the state of the art estimator for the mixed (random parameters) logit model. In all cases, there are a variety of different forms of the model available.
NLOGIT contains all of the discrete choice estimators supported by LIMDEP, plus the extensions of the discrete choice models which do not appear in LIMDEP. These include:

● Multinomial logit - many specifications
● Random effects MNL
● Nested logit
● Generalized nested logit
● Multinomial probit
● Mixed (random parameters) logit
● Kernel logit
● Heteroscedastic extreme value
● Covariance heterogeneity
● Latent class

Model Specification
NLOGIT's estimation programs are accessed as LIMDEP model commands. Since discrete choice models are often more complicated to specify than other single equation models in LIMDEP, the command setup includes many specifications that are specific to NLOGIT.

Inference Tools for Hypothesis Testing
The full set of post estimation and analysis tools in LIMDEP is accessed by NLOGIT. This includes the Wald, likelihood ratio and Lagrange multiplier tests as well as all the matrix algebra and scientific calculator tools. NLOGIT also provides tools specific for discrete choice analysis, including a built-in procedure for testing the IIA assumption of the multinomial logit model.

Simulation
Any model estimated by NLOGIT can be subjected to ‘what if' analyses using the model simulation package. The base case model produces a set of fitted probabilities for the sample data which aggregate to a prediction of the sample shares for the alternatives in the choice set. The simulator is then used, with the estimation data set or any other compatible data set, to recompute these shares under scenarios that you specify, such as a change in the price of a particular alternative or a change in household incomes.

Features of NLOGIT include:
With over 200 built-in estimators, you can analyze:
● Four level nested logit models
● Random parameters mixed logit
● Latent class
● Multinomial probit
● Panel data - fixed effects MNL
● Stated choice experiments
● Willingness to pay
● Heteroscedastic extreme value
● Best/worst modeling
● Random regret
● Attribute nonattendance
● Estimation and simulation and much more

In addition, there are many new features in Version .We have added several enhancements to give you greater flexibility in analyzing different types of data. Many of the features of NLOGIT, existing and new, are designed to let you go beyond just computing coefficients, to analyzing and using your model. We have added many new models including the random regret logit model and best/worst outcome. NLOGIT 6 continues to pioneer new developments for estimation in WTP (willingness to pay) space. Altogether, we have added dozens of features in NLOGIT 6, some clearly visible ones such as the new models and some ‘behind the scenes’ that will smooth the operation and help to stabilize the estimation programs. The following will summarize the important new developments.





New Multinomial Choice Models
NLOGIT includes many new commands and extension of the random parameters model and latent class models:
? Fixed effects in multinomial logit models
? Random effects multinomial logit models
? Random regret logit model
? Best/worst outcome data
? Berry, Levinsohn and Pakes random parameters logit model
? Latent classes with random parameters
? Generalized mixed logit







Model Extensions
? Willingness to pay
? Attribute nonattendance (explicit and implicit)
? Individual specific expected parameters
? Model simulation
? Estimated elasticities and partial effects
? Robust covariance matrix
? Random data generators
? Posterior estimates from latent class models
? Coefficients in random parameters models
? Simplified WALD command